From REF , it can be seen that we use the encoder-decoder scheme. For the encoder, we choose the ResNet [1]} for its identity mapping tackling the vanishing gradient problem in deeper network. Another benefit of ResNet is its large receptive field [2]}, which is a crucial factor for depth estimation and semantic segmentation. We also adopt dilation strategy [3]}. With the last two 2-dilated and 4-dilated residual blocks, the original resolution is lowered to \(1/8\)  instead of \(1/32\) , reducing the detailed information loss [4]} of overdownsampling.
